Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	127,920
2	South Dakota	122,030
3	Rhode Island	108,025
4	Utah	107,288
5	Tennessee	103,778
6	Arizona	103,148
7	Wisconsin	101,226
8	Iowa	100,691
9	Nebraska	98,131
10	Oklahoma	97,098
11	Arkansas	96,945
12	Kansas	95,294
13	Indiana	92,945
14	Alabama	92,915
15	Mississippi	91,615
16	Idaho	90,921
17	Nevada	89,774
18	Wyoming	89,311
19	Illinois	88,633
20	Montana	87,316
21	Louisiana	85,457
22	South Carolina	84,610
23	California	83,318
24	Georgia	82,460
25	New Mexico	82,409
26	Minnesota	81,532
27	Kentucky	81,054
28	Texas	81,013
29	Missouri	80,100
30	Florida	79,084
31	Delaware	79,007
32	New Jersey	77,376
33	Ohio	76,018
34	Massachusetts	75,045
35	Alaska	73,413
36	New York	71,763
37	North Carolina	71,461
38	Connecticut	70,127
39	Colorado	68,725
40	West Virginia	66,661
41	Pennsylvania	65,606
42	Michigan	60,797
43	Virginia	58,334
44	Maryland	58,025
45	District of Columbia	51,596
46	New Hampshire	47,685
47	Washington	41,239
48	Puerto Rico	39,635
49	Oregon	33,603
50	Maine	28,874
51	Vermont	18,683
52	Hawaii	18,080

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	774
2	Arizona	740
3	Mississippi	679
4	Texas	667
5	Alabama	657
6	Oklahoma	656
7	Rhode Island	642
8	New York	635
9	Georgia	628
10	Louisiana	624
11	Kansas	608
12	North Carolina	596
13	Arkansas	593
14	Kentucky	591
15	New Jersey	583
16	Virginia	569
17	Pennsylvania	561
18	Utah	549
19	Massachusetts	535
20	Delaware	494
21	California	489
22	Connecticut	479
23	Florida	478
24	West Virginia	462
25	Ohio	446
26	Tennessee	446
27	New Hampshire	408
28	New Mexico	397
29	Nevada	387
30	Indiana	380
31	District of Columbia	337
32	Montana	334
33	Missouri	330
34	Maryland	328
35	Idaho	327
36	Illinois	318
37	Wyoming	309
38	Wisconsin	305
39	Colorado	295
40	Iowa	283
41	Maine	274
42	Washington	269
43	Nebraska	264
44	Alaska	256
45	South Dakota	216
46	Michigan	211
47	Vermont	199
48	North Dakota	197
49	Minnesota	192
50	Oregon	187
51	Puerto Rico	93
52	Hawaii	76

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,407
2	New York	2,201
3	Massachusetts	2,095
4	Rhode Island	2,033
5	Mississippi	2,010
6	South Dakota	1,998
7	Connecticut	1,976
8	North Dakota	1,894
9	Louisiana	1,893
10	Arizona	1,792
11	Pennsylvania	1,681
12	Illinois	1,668
13	Arkansas	1,600
14	New Mexico	1,549
15	Alabama	1,543
16	Michigan	1,542
17	Indiana	1,474
18	Iowa	1,450
19	Tennessee	1,372
20	Nevada	1,371
21	South Carolina	1,348
22	Kansas	1,297
23	District of Columbia	1,285
24	Georgia	1,276
25	Texas	1,269
26	Florida	1,222
27	Maryland	1,170
28	Missouri	1,166
29	Montana	1,148
30	West Virginia	1,119
31	Delaware	1,107
32	Minnesota	1,105
33	Wisconsin	1,097
34	Nebraska	1,037
35	Wyoming	1,029
36	California	1,017
37	Colorado	988
38	Idaho	966
39	Ohio	947
40	North Carolina	878
41	Oklahoma	877
42	Kentucky	875
43	New Hampshire	761
44	Virginia	747
45	Washington	573
46	Puerto Rico	567
47	Utah	516
48	Oregon	463
49	Maine	424
50	Alaska	345
51	Hawaii	285
52	Vermont	275

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alabama	45
2	Arizona	25
3	South Carolina	23
4	South Dakota	23
5	Kansas	17
6	California	16
7	Pennsylvania	16
8	Arkansas	15
9	Georgia	15
10	Mississippi	14
11	Nevada	14
12	Tennessee	14
13	Texas	14
14	West Virginia	14
15	Montana	13
16	Louisiana	12
17	New Mexico	12
18	Oklahoma	12
19	North Carolina	11
20	Connecticut	10
21	Kentucky	10
22	Massachusetts	10
23	New Hampshire	10
24	New Jersey	10
25	Florida	9
26	New York	9
27	District of Columbia	8
28	Iowa	8
29	Rhode Island	8
30	Virginia	8
31	Illinois	7
32	Wisconsin	7
33	Idaho	6
34	Maryland	6
35	Ohio	6
36	Indiana	5
37	Washington	5
38	Colorado	4
39	Delaware	4
40	Missouri	4
41	Utah	4
42	Michigan	3
43	Minnesota	3
44	Nebraska	3
45	Oregon	3
46	Puerto Rico	3
47	Maine	2
48	Hawaii	1
49	North Dakota	1
50	Alaska	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	297,476	1	99
Chattahoochee	Georgia	239,479	2	99
Dewey	South Dakota	235,743	3	99
Lincoln	Arkansas	233,031	4	99
Bent	Colorado	230,769	5	99
Davidson	Tennessee	120,930	239	92
Richland	South Carolina	84,676	1378	56
York	South Carolina	77,835	1735	44
Orange	California	76,586	1789	43
Pierce	Washington	38,466	2894	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Buffalo	South Dakota	6,626	3	99
Dickey	North Dakota	6,568	4	99
Gregory	South Dakota	6,452	5	99
Davidson	Tennessee	1,046	1905	39
Richland	South Carolina	1,003	1975	37
Orange	California	937	2072	34
York	South Carolina	779	2279	27
Pierce	Washington	516	2645	15

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.